Manufacturers can gain market share and improve their revenues by taking advantage of the latest advancements to address evolving customer needs. For high-tech manufacturers, it’s vital to be at the forefront of cutting-edge technology and innovations. Customers around the world depend on such companies to keep their businesses running smoothly.

That’s why Konica Minolta Japan invested in an analytics platform powered by SAS. In less than a year, the company sped up its PDCA [plan-do-check-act] cycle by not only creating but deploying multiple analytical models to improve areas as diverse as malfunction forecasts and optimization of management.

When we looked at further use of sensor data, we found that SAS supports the entire series of steps in the analytics life cycle, offers high-speed processing of considerable amounts of data and has excellent forecasting accuracy.Shouichi YabeDirector of Data Science ImplementationKonica Minolta Japan

The journey to incorporating artificial intelligence (AI) and the Internet of Things (IoT)

Konica Minolta Japan is the distributor and service provider arm of Konica Minolta Inc., which primarily manufactures multifunctional peripherals, like copiers and digital print systems. The company’s goal is to develop a one-stop organization where customers can solve their office and printing issues, thereby fostering trust and loyalty.

An industry leader, Konica Minolta Japan believes in the importance of incorporating AI and sensor data into its operations. The company implemented SAS to enhance business efficiency and customer satisfaction. Shouichi Yabe, Director of the Data Science Implementation Group, oversaw Konica Minolta Japan’s SAS AI and IoT project.

After assessing the issues faced by numerous business units, as well as the relevant systems and data sources around the company, Yabe consolidated the information into an in-company resource. The time he spent interacting with the various groups helped him develop stronger relationships with employees. Through these activities, Yabe identified management issues and workplace challenges that could be resolved through analytics. He took the employee feedback and developed a system that analyzes five key areas: sales forecasts, inventory forecasts, malfunction forecasts, customer-defection forecasts and management optimization.

“When we considered adopting a new system, we ran tests on processing performance using the massive amount of data that we normally use in business,” Yabe says. “The results we obtained by creating actual analysis models revealed that only SAS could attain the processing speeds and strong forecasting accuracy at a level that could withstand use in our business environment. When we looked at further use of sensor data, we found that SAS supports the entire series of steps in the analytics life cycle, offers high-speed processing of considerable amounts of data and has excellent forecasting accuracy. From data preparation to building, executing and verifying results of analytical models, SAS not only speeds up our PDCA cycle, but also enables global expansion of the data. This is very important in terms of future expandability.”

Yabe subsequently decided to adopt the SAS Platform in a configuration that includes AI, machine learning and visual analytics. The system started operating within a short time. He soon created various analytical models and deployed them to resolve actual business issues. Repeating this PDCA cycle helped improve the models.

Konica Minolta Japan – Facts & Figures

1947

year founded

3,500+

employees in Japan

Service provider

of Konica Minolta Inc.

Measures to improve customer satisfaction

Konica Minolta Japan first applied SAS to the optimization of consumable parts replacement, like toner. In the past, it was standard practice to retain enough space for three spare toner cartridges per customer. But sometimes customers didn’t want the toner taking up their office space, or they would discard the toner without using it because its expiration date had passed. After adopting SAS, Konica Minolta Japan was able to send these consumable parts at the customer’s rate of consumption. SAS made this possible through forecasts attained by machine learning, using data from multifunctional devices and other external data. This translated into improved customer satisfaction and cost reduction by minimizing the space previously used for storing toner and the time and trouble of delivering it.

Similarly, Konica Minolta Japan established proactive maintenance to exceed customer expectations by helping customers avoid problems – not just fix them after they've occurred. Now the manufacturer can forecast the life of parts based on the status of use and send service personnel to the customer before a problem develops.

Linking technology with humans for long-term success

The SAS-sponsored projects are accelerating, and Yabe is tackling new challenges. He is a firm believer in connecting analytics, AI and people to achieve optimal results.

“It’s important for project members and management to have firsthand experience in the workplace, where they’ve actually seen the issues that employees and customers face, and truly understand their impact,” Yabe says. “This makes it possible to train models using trial and error.”

Konica Minolta Japan also uses SAS Visual Analytics, which provides management with dynamic, interactive reports. Modeling of the essential business units will shift in the future to more detailed segments of each, and then it will be possible to analyze the entire company while organically interweaving all the models.

“By using analytics, we’re attaining business benefits that far surpass our investment,” Yabe says. “SAS makes it possible for us to create closer links among management, the analytics team and the workplace. Every day, more people consult with us from different parts of the company. We are developing stronger relationships internally as well as with our customers, which is essential to our success and longevity as a business.”

The results illustrated in this article are specific to the particular situations, business models, data input, and computing environments described herein. Each SAS customer’s experience is unique based on business and technical variables and all statements must be considered non-typical. Actual savings, results, and performance characteristics will vary depending on individual customer configurations and conditions. SAS does not guarantee or represent that every customer will achieve similar results. The only warranties for SAS products and services are those that are set forth in the express warranty statements in the written agreement for such products and services. Nothing herein should be construed as constituting an additional warranty. Customers have shared their successes with SAS as part of an agreed-upon contractual exchange or project success summarization following a successful implementation of SAS software. Brand and product names are trademarks of their respective companies.